Summarization with LangChain using LLM - Stuff - Map_reduce - Refine

This tutorial focus on summarization techniques in LangChain.It covers the basic usage of document summarization techniques and provides insights into various summarization methods.
Additionally, to learn more and to explore how to validate intermediate results from the output of each of these techniques.
We dive into three key summarization techniques:
1. stuff
2. map_reduce
3. refine
Exploring their unique advantages, limitations, and ideal use cases. Whether you're dealing with large volumes of text or need to refine your summaries iteratively.
⭐️ Contents ⭐️
00:00 Introduction
6:46 Summarizer - Stuff Method
10:55 Summarizer - Map_reduce
17:06 Summarizer - Refine
Complete Article: abonia1.github.io/blogs/2023-...
📚 Resources 📚
▸ Code used in the tutorial: github.com/Abonia1/Langchain-...
▸ Langchain Summarization - official documentation: python.langchain.com/docs/use...
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[@zerofive3699]

Really useful info mam , keep up the good work

[@AboniaSojasingarayar]

It's my pleasure.

[@vijaygandhi7313]

In the abstractive summarization use-case, usually a lot of focus is given to the LLMs being used and its performance. Limitations of LLM including context length and ways to overcome this issue are often overlooked. Its important to make sure that our application is scalable when dealing with large document sizes. Thank you for this great and insightful video.

[@AboniaSojasingarayar]

Thank you Vijay Gandhi, for your insightful comment! You've raised an excellent point about the importance of considering the limitations of LLMs in the context of abstractive summarization, especially regarding their context length and scalability issues when dealing with large documents.
Indeed, one of the significant challenges in using LLMs for abstractive summarization is their inherent limitation in processing long texts due to the maximum token limit imposed by these models. This constraint can be particularly problematic when summarizing lengthy documents or articles, where the full context might not fit within the model's capacity.

[@evellynnicolemachadorosa2666]

hello! Thanks for the video. I am from Brazil. What would you recommend for large documents, averaging 150 pages? I tried map-reduce, but the inference time was 40 minutes. Are there any tips for these very long documents?

[@AboniaSojasingarayar]

Thanks for you kind words and glad this helped.
Implement a strategy that combines semantic chunking with K-means clustering to address the model’s contextual limitations. By employing efficient clustering techniques, we can extract key passages effectively, thereby reducing the overhead associated with processing large volumes of text. This approach not only significantly lowers costs by minimizing the number of tokens processed but also mitigates the recency and primacy effects inherent in LLMs, ensuring a balanced consideration of all text segments.

[@Coff03]

Did you use OpenAI API key here?

[@AboniaSojasingarayar]

Here we use open-source Mixtral from ollama.But, yes we can use OpenAI models as well.